# How is the p-value related to the alternate hypothesis?

## How is the p-value related to the alternate hypothesis?

The p-value is used as an alternative to rejection points to provide the smallest level of significance at which the null hypothesis would be rejected. A smaller p-value means that there is stronger evidence in favor of the alternative hypothesis.

## Does the p-value depend on the alternative hypothesis?

If your P value is less than the chosen significance level then you reject the null hypothesis i.e. accept that your sample gives reasonable evidence to support the alternative hypothesis….P Values.

DECISION | ||
---|---|---|

beta | 1-beta (power) | |

H0 = null hypothesis | ||

P = probability |

**Is the p-value calculated assuming the alternative hypothesis is true?**

Nope. The P value is computed assuming that the null hypothesis is true, so cannot be the probability that it is true. P value tells you how rarely you would observe a difference as larger or larger than the one you observed if the null hypothesis were true.

### What is alternative hypothesis in hypothesis testing?

A hypothesis test uses sample data to determine whether to reject the null hypothesis. The alternative hypothesis is what you might believe to be true or hope to prove true.

### What is the decision rule when using the p-value approach to hypothesis testing?

If the P-value is less than (or equal to) , reject the null hypothesis in favor of the alternative hypothesis. If the P-value is greater than , do not reject the null hypothesis.

**How do you interpret the p-value for at test?**

The smaller the p-value, the stronger the evidence that you should reject the null hypothesis.

- A p-value less than 0.05 (typically ≤ 0.05) is statistically significant.
- A p-value higher than 0.05 (> 0.05) is not statistically significant and indicates strong evidence for the null hypothesis.

#### What is the decision rule when using the P value approach to hypothesis testing?

#### What does P value of 0.05 mean?

P > 0.05 is the probability that the null hypothesis is true. A statistically significant test result (P ≤ 0.05) means that the test hypothesis is false or should be rejected. A P value greater than 0.05 means that no effect was observed.

**How do we find the p value?**

If your test statistic is positive, first find the probability that Z is greater than your test statistic (look up your test statistic on the Z-table, find its corresponding probability, and subtract it from one). Then double this result to get the p-value.

## How do you interpret a p value?

To interpret a “statistically significant” P value, you need to take into account the context of the experiment, as expressed by the prior probability that your hypothesis is true.

## How to determine p value?

Left-tailed test: p-value = Pr (S ≤ x|H 0)

**How do you calculate the p value?**

The p-value is calculated using the sampling distribution of the test statistic under the null hypothesis, the sample data, and the type of test being done (lower-tailed test, upper-tailed test, or two-sided test).

### How do you find the p value?

To find the p-value, or the probability associated with a specific observation, you must first calculate the z score, also known as the test statistic. The formula for finding the test statistic depends on whether the data includes means or proportions. The formulas we’ll discuss assume a: Large sample size.